Min Hua
Papers
2
Total Citations
44
H-Index
2
About
Min Hua’s research lies at the critical intersection of artificial intelligence and intelligent transportation systems, with a primary focus on multi-agent reinforcement learning (MARL) for connected and automated vehicles (CAVs). Her work addresses one of the most pressing challenges in modern transportation: how to coordinate multiple autonomous vehicles to achieve safe, efficient, and eco-friendly operations. In her highly cited 2025 paper, Hua provides a comprehensive survey of recent MARL advancements for CAV control, highlighting the complexities of interconnectivity and coordination that define this emerging field. This work has already garnered 37 citations, reflecting its timely importance. An earlier 2023 paper laid the groundwork for these ideas, earning 7 citations and establishing her as a consistent voice in this domain. By systematically reviewing state-of-the-art methods and identifying future research directions, Hua’s contributions help bridge the gap between theoretical reinforcement learning and real-world autonomous driving systems. Her research is essential reading for anyone interested in how AI can transform transportation, making roads safer and more sustainable through intelligent vehicle coordination.
Research Focus
Key Achievements
Top Papers
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